Asthma is a common disease characterized by airway inflammation, reversible airway obstruction, and airway hyperresponsiveness. There is growing recognition of phenotypic heterogeneity in asthma, including in severity and response to medication. It is likely that these differences are driven by distinct underlying molecular phenotypes, the discovery of which would inform the development of targeted therapies. In prior studies using well-characterized patients with asthma, differences in gene expression from human airway epithelial cells identified two major groups of patients, one in which gene expression is strongly driven by the inflammatory the T-helper cell (Th2) cytokine interleukin-13 (IL-13), which has been called Th2 "high" asthma, and another in which IL-13 driven genes are not significantly expressed above that level found in healthy controls, or "Th2 low" asthma. This work demonstrated that compared to Th2-low asthmatics, Th2-high subjects a better response to inhaled corticosteroids. Based on published data and our own preliminary data, we hypothesize that Th1 and Th17 pathways of inflammation define additional molecular phenotypes of human asthma. Our aims are to test this hypothesis, associate phenotypes to clinical characteristics including response to corticosteroids, and develop diagnostic tools for molecular phenotyping of human subjects based on Th1, Th2 and Th17 signatures. Th1, Th2, and Th17 signatures will be developed from microarray- based whole genome expression-profiling of cytokine-stimulated epithelial cell lines. These signatures will be used to cluster research asthma subjects into T helper subset phenotypes based on the gene expression profiles of their own airway epithelial cells obtained. The most informative epithelial cell genes will be used to develop a PCR-based test. A non-invasive test for these molecular phenotypes will be developed through the use of machine learning algorithms to find patterns in the whole genome expression profiles of subjects'peripheral blood samples. The identification of molecular phenotypes, and the availability of reliable tests to assign Th2, Th1, or Th17 phenotypes non-invasively would provide the potential to predict which medication will work best for an individual while avoiding unnecessary side effects form ineffective therapies, and to inform the development of therapies targeted to specific phenotypes by providing biomarkers during clinical trials. PUBLIC HEALTH RELEVANCE: Asthma is a very common lung disease affecting 7% of adults in America, and despite high treatment costs, up to 30% of patients do not respond to medications. Asthma has many forms with varying triggers, long-term outcomes and responses to medications. The goals of this work are to 1) apply technological advances in human genomics to discover distinct classes of asthma, and 2) develop tests based on these classes to predict which individuals will respond to a specific therapy, leading to more efficient care and less side effects from ineffective medicines.